Search Results for author:
Found 37 papers, 26 papers with code
FireFlow: Fast Inversion of Rectified Flow for Image Semantic Editing
Though Rectified Flows (ReFlows) with distillation offers a promising way for fast sampling, its fast inversion transforms images back to structured noise for recovery and following editing remains unsolved.
Ranked #9 on
Text-based Image Editing
on PIE-Bench
Z-STAR+: A Zero-shot Style Transfer Method via Adjusting Style Distribution
In contrast to existing approaches, we have discovered that latent features in vanilla diffusion models inherently contain natural style and content distributions.
AnchorCrafter: Animate CyberAnchors Saling Your Products via Human-Object Interacting Video Generation
The automatic generation of anchor-style product promotion videos presents promising opportunities in online commerce, advertising, and consumer engagement.
Interactive Visual Assessment for Text-to-Image Generation Models
To address these limitations, we propose DyEval, an LLM-powered dynamic interactive visual assessment framework that facilitates collaborative evaluation between humans and generative models for text-to-image systems.
HeadRouter: A Training-free Image Editing Framework for MM-DiTs by Adaptively Routing Attention Heads
Diffusion Transformers (DiTs) have exhibited robust capabilities in image generation tasks.
Visual-Friendly Concept Protection via Selective Adversarial Perturbations
Qualitative and quantitative experiments validate that VCPro achieves a better trade-off between the visibility of perturbations and protection effectiveness, effectively prioritizing the protection of target concepts in images with less perceptible perturbations.
Revealing the Two Sides of Data Augmentation: An Asymmetric Distillation-based Win-Win Solution for Open-Set Recognition
In this paper, we reveal the two sides of data augmentation: enhancements in closed-set recognition correlate with a significant decrease in open-set recognition.
U-VAP: User-specified Visual Appearance Personalization via Decoupled Self Augmentation
Extensive experiments on various kinds of visual attributes with SOTA personalization methods show the ability of the proposed method to mimic target visual appearance in novel contexts, thus improving the controllability and flexibility of personalization.
Break-for-Make: Modular Low-Rank Adaptations for Composable Content-Style Customization
Based on the adapters broken apart for separate training content and style, we then make the entity parameter space by reconstructing the content and style PLPs matrices, followed by fine-tuning the combined adapter to generate the target object with the desired appearance.
Make-Your-Anchor: A Diffusion-based 2D Avatar Generation Framework
We adopt a two-stage training strategy for the diffusion model, effectively binding movements with specific appearances.
CreativeSynth: Creative Blending and Synthesis of Visual Arts based on Multimodal Diffusion
Large-scale text-to-image generative models have made impressive strides, showcasing their ability to synthesize a vast array of high-quality images.
Z*: Zero-shot Style Transfer via Attention Reweighting
Despite the remarkable progress in image style transfer formulating style in the context of art is inherently subjective and challenging.
MotionCrafter: One-Shot Motion Customization of Diffusion Models
The essence of a video lies in its dynamic motions, including character actions, object movements, and camera movements.
Adversarial Robust Memory-Based Continual Learner
Despite the remarkable advances that have been made in continual learning, the adversarial vulnerability of such methods has not been fully discussed.
Topology-preserving Adversarial Training for Alleviating Natural Accuracy Degradation
Despite the effectiveness in improving the robustness of neural networks, adversarial training has suffered from the natural accuracy degradation problem, i. e., accuracy on natural samples has reduced significantly.
$Z^*$: Zero-shot Style Transfer via Attention Rearrangement
Despite the remarkable progress in image style transfer, formulating style in the context of art is inherently subjective and challenging.
Dance Your Latents: Consistent Dance Generation through Spatial-temporal Subspace Attention Guided by Motion Flow
The advancement of generative AI has extended to the realm of Human Dance Generation, demonstrating superior generative capacities.
ProSpect: Prompt Spectrum for Attribute-Aware Personalization of Diffusion Models
We apply ProSpect in various personalized attribute-aware image generation applications, such as image-guided or text-driven manipulations of materials, style, and layout, achieving previously unattainable results from a single image input without fine-tuning the diffusion models.
Progressive Open Space Expansion for Open-Set Model Attribution
In this study, we focus on a challenging task, namely Open-Set Model Attribution (OSMA), to simultaneously attribute images to known models and identify those from unknown ones.
A Unified Arbitrary Style Transfer Framework via Adaptive Contrastive Learning
Our framework consists of three key components, i. e., a parallel contrastive learning scheme for style representation and style transfer, a domain enhancement module for effective learning of style distribution, and a generative network for style transfer.
Region-Aware Diffusion for Zero-shot Text-driven Image Editing
Different from current mask-based image editing methods, we propose a novel region-aware diffusion model (RDM) for entity-level image editing, which could automatically locate the region of interest and replace it following given text prompts.
Inversion-Based Style Transfer with Diffusion Models
Our key idea is to learn artistic style directly from a single painting and then guide the synthesis without providing complex textual descriptions.
Ranked #5 on
Style Transfer
on StyleBench
DiffStyler: Controllable Dual Diffusion for Text-Driven Image Stylization
Despite the impressive results of arbitrary image-guided style transfer methods, text-driven image stylization has recently been proposed for transferring a natural image into a stylized one according to textual descriptions of the target style provided by the user.
Draw Your Art Dream: Diverse Digital Art Synthesis with Multimodal Guided Diffusion
Extensive experimental results on the quality and quantity of the generated digital art paintings confirm the effectiveness of the combination of the diffusion model and multimodal guidance.
Adaptive Assignment for Geometry Aware Local Feature Matching
The detector-free feature matching approaches are currently attracting great attention thanks to their excellent performance.
Domain Enhanced Arbitrary Image Style Transfer via Contrastive Learning
Our framework consists of three key components, i. e., a multi-layer style projector for style code encoding, a domain enhancement module for effective learning of style distribution, and a generative network for image style transfer.
Ranked #4 on
Style Transfer
on StyleBench
SIOD: Single Instance Annotated Per Category Per Image for Object Detection
Object detection under imperfect data receives great attention recently.
CrossRectify: Leveraging Disagreement for Semi-supervised Object Detection
Semi-supervised object detection has recently achieved substantial progress.
StyTr2: Image Style Transfer With Transformers
The goal of image style transfer is to render an image with artistic features guided by a style reference while maintaining the original content.
Ranked #3 on
Style Transfer
on StyleBench
DAE-GAN: Dynamic Aspect-aware GAN for Text-to-Image Synthesis
Text-to-image synthesis refers to generating an image from a given text description, the key goal of which lies in photo realism and semantic consistency.
StyTr$^2$: Image Style Transfer with Transformers
The goal of image style transfer is to render an image with artistic features guided by a style reference while maintaining the original content.
Unveiling the Potential of Structure Preserving for Weakly Supervised Object Localization
Weakly supervised object localization(WSOL) remains an open problem given the deficiency of finding object extent information using a classification network.
Arbitrary Video Style Transfer via Multi-Channel Correlation
Towards this end, we propose Multi-Channel Correction network (MCCNet), which can be trained to fuse the exemplar style features and input content features for efficient style transfer while naturally maintaining the coherence of input videos.
Distribution Aligned Multimodal and Multi-Domain Image Stylization
Currently, there are few methods that can perform both multimodal and multi-domain stylization simultaneously.
Arbitrary Style Transfer via Multi-Adaptation Network
Arbitrary style transfer is a significant topic with research value and application prospect.
Multi-Attribute Guided Painting Generation
Controllable painting generation plays a pivotal role in image stylization.
Image Retargetability
In this work, we introduce the notion of image retargetability to describe how well a particular image can be handled by content-aware image retargeting.
